The present invention relates to neural-emulating computing systems and, more particularly, to a neural computing network including a plurality of neural circuits and producing a single winner-take-all output at the "winning" circuit comprising, a common line supplying current and producing a maximum voltage potential; and wherein each neural circuit comprises, follower transistor means operably connected to the common line for sourcing current, the follower transistor means including gate means for connection to a current signal input source providing a current signal to each neural circuit to be compared to the current signals at respective ones of the other neural circuits; and, inhibitor transistor means having a gate operably connected to the common line and being operably connected to the gate means of the follower transistor means for providing a voltage output of the neural circuit at an output thereof and for inhibiting the voltage output at all neural circuits connected to the common line which have a current signal which is smaller than the largest the current signal connected to one of the neural circuits.
Computing systems have gone through several evolutionary processes as the art itself has developed and as new technology has permitted the art to develop in new directions. Much early systems type simulation, testing, and decision making was accomplished employing analog computing devices. While large and cumbersome as well as being tedious to "program" (actually accomplished by physically interconnecting electronic elements by means of plug in wires on a so-called patch board) analog computers did have the advantage of providing decisions by degree. A binary device (such as a switch) is either on or off. It is either black or white, i.e. no degrees of gray. An analog output, on the other hand, covers black, white, and various degrees of gray in between. Thus, one can tell that the answer to a system question is not only true; but, how true--i.e., very true, marginally true, etc.
With the advent of small, fast, easily programmed digital computers and a variety of dependable high level languages, analog computers virtually fell into disuse. While a digital computer operates in serial and, therefore, is not basically adapted to parallel decision making (as are analog computers), with smaller problems, multi-processor configurations are able to fill the bill. This simply means that a plurality of interconnected computers are assigned to work on the same problem simultaneously. In distributed processing environments where a number of computers (often referred to as nodes, workstations, or the like,) are interconnected on a local area network (LAN) it is not unusual to have several hundred nodes working a common problem. So-called "hypercube" systems now in development anticipate having several thousand processors ultimately interconnected and communicating with one another to perform computations in space applications, for example, where such extensive parallel processing is required to provide real-time answers.
In the continuing task of trying to design and construct computers which can duplicate (or at least closely approximate) the human brain in its thinking and learning processes, there has been a switch back towards analog computational techniques because of their closer functional proximity to the brain. The neurons of the brain are interconnected by the synapses to provide a large analog computational capability that provides instant decisional responses to changes in input stimuli in the same manner as an analog computer. The human brain does not perform a concurrent digital computational process on the input data. Thus, much research is presently being accomplished with so-call "neural networks" which are electronic networks which functionally simulate the neurons and synapses of the brain.
A neural network may comprise many many thousands of simulated neurons. Activity in neural systems is mediated by two general types of inhibition: subtractive, which may be thought of as setting the zero level for the computation, and multiplicative (non-linear), which regulates the gain of the computation. What is lacking at this point is circuitry to be included within the simulated neural paths which can provide a general non-linear inhibition in its extreme form, known as winner-take-all--a circuit that will indicate not only the path with the highest input stimulation value but, additionally, provide an indication of the degree of "winning".
Wherefore, it is the object of the present invention to provide a winner-take-all circuit that can be employed in the various paths comprising a neural network computing system, or the like, which will indicate not only the path with the highest stimulation input value at any instant but, additionally, provide an indication of the degree of "winning".
Other objects and benefits of the present invention will become apparent from the detailed description which follows hereinafter when taken in conjunction with the drawing figures that accompany it.